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20202025
most citedDialogue State Induction Using Neural Latent Variable Models

14 citations · 29 across the 9 of their papers we have counts for

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cs.CL2025

AutoPR: Let's Automate Your Academic Promotion!

Qiguang Chen, Zheng Yan, Mingda Yang +10

As the volume of peer-reviewed research surges, scholars increasingly rely on social platforms for discovery, while authors invest considerable effort in promoting their work to en…

cs.CL2025

Beyond Surface Reasoning: Unveiling the True Long Chain-of-Thought Capacity of Diffusion Large Language Models

Qiguang Chen, Hanjing Li, Libo Qin +7

Recently, Diffusion Large Language Models (DLLMs) have offered high throughput and effective sequential reasoning, making them a competitive alternative to autoregressive LLMs (ALL…

cs.CL2025

Aware First, Think Less: Dynamic Boundary Self-Awareness Drives Extreme Reasoning Efficiency in Large Language Models

Qiguang Chen, Dengyun Peng, Jinhao Liu +4

Recent advancements in large language models (LLMs) have greatly improved their capabilities on complex reasoning tasks through Long Chain-of-Thought (CoT). However, this approach…

cs.CL20253 cited

AI4Research: A Survey of Artificial Intelligence for Scientific Research

Qiguang Chen, Mingda Yang, Libo Qin +13

Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs) such as OpenAI-o1 and DeepSeek-R1, have demonstrated remarkable capabilities in co…

cs.CL2025

MPCC: A Novel Benchmark for Multimodal Planning with Complex Constraints in Multimodal Large Language Models

Yiyan Ji, Haoran Chen, Qiguang Chen +3

Multimodal planning capabilities refer to the ability to predict, reason, and design steps for task execution with multimodal context, which is essential for complex reasoning and…

cs.CL2025

Electronic Circuit Principles of Large Language Models

Qiguang Chen, Libo Qin, Jinhao Liu +6

Large language models (LLMs) such as DeepSeek-R1 have achieved remarkable performance across diverse reasoning tasks. To uncover the principles that govern their behaviour, we intr…